科学研究传统上是人力密集型的,，要求研究人员协调文献,想法,实验,手稿,并在漫长的项目周期中审查回复。基于法学硕士的科学代理人的兴起为这一过程的自动化创造了机会。这样的系统必须支持完整的研究生命周期, 维护跨项目的结构化持久内存, 并随着时间的推移改进自己的研究程序。然而,现有系统要么部分满足，要么无法满足这些要求,为统一的自动化科学研究系统留下了空白。因此,，我们推出了 AutoSci, 一个以内存为中心的代理系统，适用于整个科学研究生命周期。 AutoSci 围绕四个模块进行组织。 SciMem 提供模式管理的研究记忆,，将可重用科学知识的长期知识记忆与项目级工件（例如想法, 实验, 手稿, 和评论）的主动研究记忆分开。 SciFlow 通过控制状态, 上下文, 验证, 反馈, 和编排的工具执行从文献理解到反驳的五个阶段的生命周期。 SciDAG 通过 DAG 形状的多智能体操作员和可重复使用的阶段特定模板来增强困难技能。 SciEvolve 将来自用户, 实验, 评论, 和外部环境的反馈信号转换为 SciMem 组织, SciFlow 技能, 和 SciDAG 模板的版本化更新。 , 这些模块共同使 AutoSci 成为一个持久的研究环境，可以执行, 记住, 并在研究项目中不断发展。代码存储库可从此 https URL 获取。
Scientific research has traditionally been human-intensive, requiring researchers to coordinate literature, ideas, experiments, manuscripts, and review responses across long project cycles. The rise of LLM-based scientific agents creates an opportunity to automate this process. Such a system must support the full research lifecycle, maintain structured persistent memory across projects, and improve its own research procedures over time. However, existing systems either partially satisfy or fail to satisfy these requirements, leaving a gap for a unified automated scientific research system. As a result, we present AutoSci, a memory-centric agentic system for the full scientific research lifecycle. AutoSci is organized around four modules. SciMem provides schema-governed research memory, separating Long-Term Knowledge Memory for reusable scientific knowledge from Active Research Memory for project-level artifacts such as ideas, experiments, manuscripts, and reviews. SciFlow executes a five-stage lifecycle from literature understanding to rebuttal through a harness that controls state, context, verification, feedback, and orchestration. SciDAG augments difficult skills with DAG-shaped multi-agent operators and reusable stage-specific templates. SciEvolve converts feedback signals from users, experiments, reviews, and external environments into versioned updates to SciMem organization, SciFlow skills, and SciDAG templates. Together, these modules make AutoSci a persistent research environment that can execute, remember, and evolve across research projects. The code repository is available at this https URL.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)